Search Results (12,329)

Sustainable Development Reserves are organized units in the Amazon that are essential for the proper use and sustainable management of the region’s natural resources and for the livelihoods and economy of the local communities. This study aims to provide a climatic characterization of the Madeira River Sustainable Development Reserve (MSDR), offering scientific support to efforts to assess the feasibility of implementing adaptation measures to increase the resilience of isolated Amazon communities in the face of extreme climate events. Significant statistical analyses based on time series of observational and reanalysis climate data were employed to obtain a detailed diagnosis of local climate variability. The results show that monthly mean two-meter temperatures vary from 26.5 °C in February, the coolest month, to 28 °C in August, the warmest month. Monthly precipitation averages approximately 250 mm during the rainy season, from December until May. July and August are the driest months, August and September are the warmest months, and September and October are the months with the lowest river level. Cold spells were identified in July, and warm spells were identified between July and September, making this period critical for public health. Heavy precipitation events detected by the R80, Rx1day, and Rx5days indices show an increasing trend in frequency and intensity in recent years. The analyses indicated that the MSDR has no potential for wind-energy generation; however, photovoltaic energy production is viable throughout the year. Regarding the two major commercial crops and their resilience to thermal stress, the region presents suitable conditions for açaí palm cultivation, but Brazil nut production may be adversely affected by extreme drought and heat events. The results of this study may support research on adaptation strategies that includethe preservation of local traditions and natural resources to ensure sustainable development. Full article

This study compared two approaches to integrating leveling and GNSS data to develop relative vertical movements of the Earth’s crust. Novel approaches were tested using transformation and hybrid grid adjustment. The results from double-leveling measurements in Poland were used as test data, and GNSS measurements developed using the PPP technique were used as Supplementary Data. The least squares method was used for the adjustment, and the isometric, conformal and affine methods were used for the transformation, with and without Hausbrandt correction. So-called pseudo-nodal points, i.e., points identified as common in both networks, whose weight was determined according to the assumptions of scale-free network theory, were used as integration points. Both integration methods have similar results and are suitable for integrating leveling and GNSS data to determine the relative vertical movements of the Earth’s crust. The average unit error m₀ of the transformation was 0.1 mm/yr and the average error after adjustment of the hybrid network was 0.1 mm/yr. The use of the Hausbrandt correction does not significantly improve the transformation results. A 12-parameter affine transformation is recommended as the transformation method. Full article

Fractional Vegetation Cover (FVC) is an important parameter to reflect vegetation growth and describe plant canopy structure. This study integrates both active and passive remote sensing, capitalizing on the complementary strengths of optical and radar data, and applies various machine learning algorithms to retrieve FVC. The results demonstrate that, for FVC retrieval, the optimal combination of optical remote sensing bands includes B2 (490 nm), B5 (705 nm), B8 (833 nm), B8A (865 nm), and B12 (2190 nm) from Sentinel-2, achieving an Optimal Index Factor (OIF) of 522.50. The LiDAR data of ICESat-2 imagery is more suitable for extracting FVC than that of GEDI imagery, especially at a height of 1.5 m, and the correlation coefficient with the measured FVC is 0.763. The optimal feature variable combinations for FVC retrieval vary among different vegetation types, including synthetic aperture radar, optical remote sensing, and terrain data. Among the three models tested—multiple linear regression, random forest, and support vector machine—the random forest model outperformed the others, with fitting correlation coefficients all exceeding 0.974 and root mean square errors below 0.084. Adding LiDAR data on the basis of optical remote sensing combined with machine learning can effectively improve the accuracy of remote sensing retrieval of vegetation coverage. Full article

Isobaric vapor-liquid equilibrium (VLE) data for binary mixtures of biomass–derived ethyl levulinate and ethanol were measured using an apparatus comprising a modified Rose-Williams still and a condensation system. Measurements were taken at temperatures ranging from 329.58 K to 470.00 K and pressures of 40.0, 60.0 and 80.0 kPa. The thermodynamic consistency of the VLE data was evaluated using the Redlich-Kister area test, the Fredenslund test and the Van Ness point-to-point test. The data was correlated using three activity coefficient models: Wilson, NRTL and UNIQUAC. The Gibbs energy of mixing of the VLE data was analyzed to verify the suitability of the binary interaction parameters of these models. The activity coefficients and excess Gibbs free energy, calculated from the VLE experimental data and model correlation results, were analyzed to evaluate the models’ fit and the non–ideality of the binary system. The accuracy of the regression results was also assessed based on the root mean square deviation (RMSD) and average absolute deviation (AAD) for both temperature and the vapor phase mole fraction of ethyl levulinate. The results indicate that the NRTL model provided the best fit to the experimental data. Notably, the experimental data showed strong correlation with the predictions of all three models, suggesting their reliability for practical application. Full article

The accurate measurement of the tree diameter is vital for forest inventories, urban tree quality assessments, the management of roadside and railway vegetation, and various other applications. It also plays a crucial role in evaluating tree growth dynamics, which are closely linked to tree health, structural stability, and vulnerability. Although a range of devices and methodologies are currently under investigation, the widespread adoption of laser scanners remains constrained by their high cost. This study therefore aimed to compare high-end laser scanners (Trimble TX8 and GeoSLAM ZEB Horizon) with cost-effective alternatives, represented by the Apple iPhone 14 Pro and the LA03 scanner developed by mapry Co., Ltd. (Tamba, Japan). It further sought to evaluate the feasibility of employing these more affordable devices, even for small-scale forest owners or managers. Given the growing availability of 3D-based forest inventory algorithms, a selection of such processing pipelines was used to assess the practical potential of the scanning devices. The tested low-cost device produced moderate results, achieving a tree detection rate of up to 78% and a relative root mean square error (rRMSE) of 19.7% in diameter at breast height (DBH) estimation. However, performance varied depending on the algorithms applied. In contrast, the high-end mobile laser scanning (MLS) and terrestrial laser scanning (TLS) systems outperformed the low-cost alternative across all metrics, with tree detection rates reaching up to 99% and DBH estimation rRMSEs as low as 5%. Nevertheless, the low-cost device may still be suitable for scanning small sample plots at a reduced cost and could potentially be deployed in larger quantities to support broader forest inventory initiatives. Full article

Olive stones (OS) are a by-product of great interest from olive oil mills and the table olive industry due to their high content of phenolic compounds. In this work, the extraction of phenolic compounds from OS via microwave-assisted extraction (MAE) with aqueous acetone was assayed. A central composite design of experiments was used to determine the optimal extraction conditions, with the independent variables being temperature, process time, and aqueous acetone (v/v). The dependent variables were the total content of phenolic compounds (TPC) measured by the Folin–Ciocalteu method and the main phenolic compounds identified and quantified by UPLC. Under optimal conditions (75 °C, 20 min, and 60% acetone), 3.32 mg TPC was extracted from 100 g of dry matter (DM) OS. The most suitable extraction conditions were different for each polyphenol. Therefore, 292.11 μg vanillin/g DM; 10.94 μg oleuropein/g DM; and 10.11 protocatechuic acid μg/g DM were obtained under conditions of 60 °C, 15 min, and 100% acetone; 43.8 °C, 10.45 min, and 61.3% acetone; and 64.8 °C, 16.58 min, and 97.8% acetone, respectively. Finally, MAE was compared with the traditional Soxhlet method under the same conditions. As a result, MAE was proven to be an enhanced and more feasible method for polyphenol extraction from OS. Full article

Gold kiwifruits from two different farms, harvested at different times, were analysed using both non-destructive and destructive methods. A computer vision system (CVS) and a portable spectroradiometer were used to perform non-destructive measurements of firmness, titratable acidity, pH, soluble solids content, dry matter, and soluble sugars (glucose and fructose), with the goal of building predictive models for the maturity index. Hyperspectral data from the visible–near-infrared (VIS–NIR) and short-wave infrared (SWIR) ranges, collected via the spectroradiometer, along with colour features extracted by the CVS, were used as predictors. Three different regression methods—Partial Least Squares (PLS), Support Vector Regression (SVR), and Gaussian process regression (GPR)—were tested to assess their predictive accuracy. The results revealed a significant increase in sugar content across the different harvesting times in the season. Regardless of the regression method used, the CVS was not able to distinguish among the different harvests, since no significant skin colour changes were measured. Instead, hyperspectral measurements from the near-infrared (NIR) region and the initial part of the SWIR region proved useful in predicting soluble solids content, glucose, and fructose. The models built using these spectral regions achieved R² average values between 0.55 and 0.60. Among the different regression models, the GPR-based model showed the best performance in predicting kiwifruit soluble solids content, glucose, and fructose. In conclusion, for the first time, the effectiveness of a fully portable spectroradiometer measuring surface reflectance until the full SWIR range for the rapid, contactless, and non-destructive estimation of the maturity index of kiwifruits was reported. The versatility of the portable spectroradiometer may allow for field applications that accurately identify the most suitable moment to carry out the harvesting. Full article

This paper presents a formulation for the dynamic analysis of dam–reservoir–foundation systems, employing a coupled finite element model that integrates displacements and reservoir pressures. An innovative coupled approach, without separating the solid and fluid equations, is proposed to directly solve the single non-symmetrical governing equation for the whole system with non-proportional damping. For the modal analysis, a state–space method is adopted to solve the coupled eigenproblem, and complex eigenvalues and eigenvectors are computed, corresponding to non-stationary vibration modes. For the seismic analysis, a time-stepping method is applied to the coupled dynamic equation, and the stress–transfer method is introduced to simulate the nonlinear behavior, innovatively combining a constitutive joint model and a concrete damage model with softening and two independent scalar damage variables (tension and compression). This formulation is implemented in the computer program DamDySSA5.0, developed by the authors. To validate the formulation, this paper provides the experimental and numerical results in the case of the Cahora Bassa dam, instrumented in 2010 with a continuous vibration monitoring system designed by the authors. The good comparison achieved between the monitoring data and the dam–reservoir–foundation model shows that the formulation is suitable for simulating the modal response (natural frequencies and mode shapes) for different reservoir water levels and the seismic response under low-intensity earthquakes, using accelerograms measured at the dam base as input. Additionally, the dam’s nonlinear seismic response is simulated under an artificial accelerogram of increasing intensity, showing the structural effects due to vertical joint movements (release of arch tensions near the crest) and the concrete damage evolution. Full article

The stability of modern power systems has become critically dependent on precise inertia estimation of synchronous generators, particularly as renewable energy integration fundamentally transforms grid dynamics. Increasing penetration of converter-interfaced renewable resources reduces system inertia, heightening the grid’s susceptibility to transient disturbances and creating significant technical challenges in maintaining operational reliability. This paper addresses these challenges through a novel Bayesian inference framework that synergistically integrates PMU data with an advanced MCMC sampling technique, specifically employing the Affine-Invariant Ensemble Sampler. The proposed methodology establishes a probabilistic estimation paradigm that systematically combines prior engineering knowledge with real-time measurements, while the Affine-Invariant Ensemble Sampler mechanism overcomes high-dimensional computational barriers through its unique ensemble-based exploration strategy featuring stretch moves and parallel walker coordination. The framework’s ability to provide full posterior distributions of inertia parameters, rather than single-point estimates, helps for stability assessment in renewable-dominated grids. Simulation results on the IEEE 39-bus and 68-bus benchmark systems validate the effectiveness and scalability of the proposed method, with inertia estimation errors consistently maintained below $1\%$ across all generators. Moreover, the parallelized implementation of the algorithm significantly outperforms the conventional M-H method in computational efficiency. Specifically, the proposed approach reduces execution time by approximately $52\%$ in the 39-bus system and by $57\%$ in the 68-bus system, demonstrating its suitability for real-time and large-scale power system applications. Full article

Security checks (SCs) at metro stations are regarded as an effective measure to address the heightened security risks associated with high ridership. Introducing SCs without exacerbating congestion requires a thorough understanding of their impact on passenger flow. Most existing studies were conducted where SCs are mandatory and fixed at certain locations. This study presents a method for advising the scale and placement for SCs under a more relaxed security setting. Using agent-based simulation with heterogeneous profiles for both inbound and outbound passenger flow, existing bottlenecks are first identified. By varying different percentages of passengers for SCs and locations to deploy SCs, we observe the influence on existing bottlenecks and suggest a suitable configuration. In our experiments, key bottlenecks are identified before tap-in fare gantries. When deploying SCs near tap-in fare gantries as seen in current practices, a screening percentage of beyond 10% could exacerbate existing bottlenecks and also create new bottlenecks at SC waiting areas. Relocating the SC to a point beyond the fare gantries helps alleviate congestion. This method provides a reference for station managers and transport authorities for balancing security and congestion. Full article

This paper proposes an adaptive human–robot concurrent control scheme that achieves the appropriate gait trajectory for a robotic leg prosthesis to improve the wearer’s comfort in various tasks. To accommodate different wearers, a neuromuscular-force-based impedance method was developed using muscle activation to reshape gait trajectory. To eliminate the use of sensors for torque measurement, a disturbance observer was established to estimate the interaction force between the human residual limb and the prosthetic receptacle. The cost function was combined with the interaction force and tracking errors of the joints. We aim to reduce the cost function by minimally changing the control weight of the gait trajectory generated by the Central Pattern Generator (CPG). The control scheme was primarily based on human-in-the-loop optimization to search for a suitable control weight to regenerate the appropriate gait trajectory. To handle the uncertainties and unknown coupling of the motors, an adaptive law was designed to estimate the unknown parameters of the system. Through a stability analysis, the control framework was verified by semi-globally uniformly ultimately bounded stability. Experimental results are discussed, and the effectiveness of the adaptive control framework is demonstrated. In Case 1, the mean error (MEAN) of the tracking performance was $3.6°$ and $3.3°$, respectively. And the minimized mean square errors (MSEs) of the tracking performance were $2.3°$ and $2.8°$, respectively. In Case 2, the mean error (MEAN) of the tracking performance is $2.7°$ and $3.1°$, respectively. And the minimized mean square errors (MSEs) of the tracking performance are $1.8°$ and $2.4°$, respectively. In Case 3, the mean errors (MEANs) of the tracking performance for subject1 and 2 are $2.4°$, $2.9°$, $3.4°$, and $2.2°$, $2.8°$, $3.1°$, respectively. The minimized mean square errors (MSEs) of the tracking performance for subject1 and 2 were $1.6°$, $2.3°$, $2.6°$, and $1.3°$, $1.7°$, $2.2°$, respectively. Full article

Winter wheat yield prediction is critical for optimizing field management plans and guiding agricultural production. To address the limitations of conventional manual yield estimation methods, including low efficiency and poor interpretability, this study innovatively proposes an intelligent yield estimation method based on a convolutional neural network (CNN). A comprehensive two-factor (fertilization × irrigation) controlled field experiment was designed to thoroughly validate the applicability and effectiveness of this method. The experimental design comprised two irrigation treatments, sufficient irrigation (C) at 750 m³ ha⁻¹ and deficit irrigation (M) at 450 m³ ha⁻¹, along with five fertilization treatments (at a rate of 180 kg N ha⁻¹): (1) organic fertilizer alone, (2) organic–inorganic fertilizer blend at a 7:3 ratio, (3) organic–inorganic fertilizer blend at a 3:7 ratio, (4) inorganic fertilizer alone, and (5) no fertilizer control. The experimental protocol employed a DJI M300 RTK unmanned aerial vehicle (UAV) equipped with a multispectral sensor to systematically acquire high-resolution growth imagery of winter wheat across critical phenological stages, from heading to maturity. The acquired multispectral imagery was meticulously annotated using the Labelme professional annotation tool to construct a comprehensive experimental dataset comprising over 2000 labeled images. These annotated data were subsequently employed to train an enhanced CNN model based on ResNet50 architecture, which achieved automated generation of panicle density maps and precise panicle counting, thereby realizing yield prediction. Field experimental results demonstrated significant yield variations among fertilization treatments under sufficient irrigation, with the 3:7 organic–inorganic blend achieving the highest actual yield (9363.38 ± 468.17 kg ha⁻¹) significantly outperforming other treatments (p < 0.05), confirming the synergistic effects of optimized nitrogen and water management. The enhanced CNN model exhibited superior performance, with an average accuracy of 89.0–92.1%, representing a 3.0% improvement over YOLOv8. Notably, model accuracy showed significant correlation with yield levels (p < 0.05), suggesting more distinct panicle morphological features in high-yield plots that facilitated model identification. The CNN’s yield predictions demonstrated strong agreement with the measured values, maintaining mean relative errors below 10%. Particularly outstanding performance was observed for the organic fertilizer with full irrigation (5.5% error) and the 7:3 organic-inorganic blend with sufficient irrigation (8.0% error), indicating that the CNN network is more suitable for these management regimes. These findings provide a robust technical foundation for precision farming applications in winter wheat production. Future research will focus on integrating this technology into smart agricultural management systems to enable real-time, data-driven decision making at the farm scale. Full article

This paper presents a multi-sensor fusion framework for the accurate indoor localization and trajectory tracking of a differential-drive mobile robot. The proposed system integrates Ultra-Wideband (UWB) trilateration, wheel odometry, and Attitude and Heading Reference System (AHRS) data using a Kalman filter. This fusion approach reduces the impact of noisy and inaccurate UWB measurements while correcting odometry drift. The system combines raw UWB distance measurements with wheel encoder readings and heading information from an AHRS to improve robustness and positioning accuracy. Experimental validation was conducted through repeated closed-loop trajectory trials. The results demonstrate that the proposed method significantly outperforms UWB-only localization, yielding reduced noise, enhanced consistency, and lower Dynamic Time Warping (DTW) distances across repetitions. The findings confirm the system’s effectiveness and suitability for real-time mobile robot navigation in indoor environments. Full article

The rubber plantations in Sankuru province, located in the Democratic Republic of Congo (DRC), have historically been pivotal to the regional economy. However, the absence of suitable silvicultural practices has promoted self-regeneration, resulting in the proliferation of diverse species. This study aims to characterize species richness and plant structure of these plantations. To this end, 80 subplots measuring 0.25 hectares were meticulously established, with a proportionate division between state-owned and farmer plantations. The results obtained from this study indicate that these plantations are home to approximately 105 species, classified into 33 distinct botanical families, with dominant families such as Fabaceae, Meliaceae, Euphorbiaceae, Olacaceae, Clusiaceae, and Moraceae. Despite the similarity between the two types of plantations (Cs = 58%), significant disparities were observed in terms of individuals, 635 ± 84.06 and 828 ± 144.62 (p < 10⁻³); species, 41 ± 7.49 and 28 ± 4.59 (p < 10⁻³); families, 19 ± 3.06 and 16 ± 1.62 (p < 10⁻²); and basal area, 29.88 ± 5.8 and 41.37 ± 7.57 (p < 10⁻²) for state and peasant plantations, respectively. State plantations exhibited greater diversity (H′ = 1.87) and enhanced equity (J’ = 0.43) than peasant plantations. The diametric structure exhibited an inverted J-shaped distribution, indicating constant and regular regeneration of these plantations. The upper canopy dominates the vertical structure in both types of plantations, with a significantly higher proportion in peasant plantations (83.60%) than in state plantations (73.8%), ANOVA (F (2.24 = 21.78), df = 24; p = 4.03 × 10⁻⁶). The findings indicate that the sustainable management of these plantations could incorporate agroecological principles to promote the coexistence of rubber production and biodiversity conservation while contributing to the restoration of degraded ecosystems and the well-being of local communities. Full article

Since the definitions of the two-dimensional (2D) optical absorption coefficient and photoconductivity are independent of the thickness of 2D materials, they are more suitable than the dielectric function to describe the optical properties of 2D materials. Based on the many-body GW method and the Bethe–Salpeter equation, we calculated the quasiparticle electronic structure, optical absorbance, and complex photoconductivity of 2D InSe from a single layer (1L) to three layers (3L). The calculation results show that the energy difference between the direct and indirect band gaps in 1L, 2L, and 3L InSe is so small that strain can readily tune its electronic structure. The 2D optical absorbance results calculated taking into account exciton effects show that light absorption increases rapidly near the band gap. Strain modulation of 1L InSe shows that it transforms from an indirect bandgap semiconductor to a direct bandgap semiconductor in the biaxial compressive strain range of −1.66 to −3.60%. The biaxial compressive strain causes a slight blueshift in the energy positions of the first and second absorption peaks in monolayer InSe while inducing a measurable redshift in the energy positions of the third and fourth absorption peaks. Full article